The identification of a specific sequence of double stranded DNA in a sample after performing a polymerase chain reaction (PCR) is often difficult. Many variables, including the test conditions, sample size and the frequency of measurements, affect the often low signal-to-noise ratio that makes data analysis difficult.
PCR is well known in the art and generally refers to an in vitro method for amplifying a specific polynucleotide template sequence. The technique of PCR is described in numerous publications, including, PCR: A Practical Approach, M. J. McPherson, et al., IRL Press (1991), PCR Protocols: A Guide to Methods and Applications, by Innis, et al., Academic Press (1990), and PCR Technology: Principals and Applications for DNA Amplification, H. A. Erlich, Stockton Press (1989), all of which are incorporated herein in their entireties for all purposes.
The use of a dye that binds with dsDNA can facilitate the identification of dsDNA in a sample because some dyes increase in fluorescence when bound to dsDNA. SYBR Green I is a well-known example of a dye with this property used to identify dsDNA in a sample. The fluorescence signal of such a dye is proportional to the total quantity of dsDNA in a sample. The use of such a dye with a melting curve enhances the ability to identify a specific sequence of dsDNA in a sample.
The melting curve measures the fluorescence of the specific sample as a function of temperature. The melting temperature of a dsDNA sequence is the temperature at which half of the dsDNA sequence dissociates into single stranded DNA. Dissociation, or denaturation, is a process by which the individual strands of the dsDNA separate into single stranded DNA (ssDNA). The combined effect that different dsDNA sequences often have different melting temperatures and the fluorescence of the dye decreases when not bound to dsDNA allows the fluorescence signal from a melting curve to be used to help determine the dsDNA sequence found in a post PCR sample.
The raw melting curve data from a PCR instrument normally consists of a set of melt curves, one for each sample. A typicality melt curve consists of a decreasing fluorescence measurement with increasing temperature, as fluorophore is quenched after being released by the separating (melting) ssDNA strands. Melting is preceded by an amplification phase, and due to this and the limits of quantification of the initial sample (and other factors), the melt curves do not all start from the same initial point.
Azbel [M. Ya Azbel, “DNA Sequencing and Melting Curve”, Proc. Natl. Acad. Sci., 76(1), pp. 101-105, 1979], which is incorporated herein by specific reference, discusses the rate of melting of a single DNA domain as a function of temperature. This can be translated into an idealized melt curve for a (short) domain, which has the form of a logistic equation:1/1+exp(λ(T−T0))   Eqn. 1where T is temperature, T0 is the temperature of maximum rate of melting, and λ is a constant controlling the spread of melting temperature (which presumably also depends on the rate of heating).
In practice this equation does not hold. However, it is important to obtain an accurate model of the Melt Curve in order to ascertain information about the dsDNA.
Furthermore, with the advent of high resolution melting (HRM) as a tool for mutation screening and discovery, analysis techniques are being sought after to allow for enhanced automated processing of larger sample sizes. At present, HRM utilizes a standard method of melt curve normalization that is typically followed by a subtraction plot, which is generated by selecting a known control. Agglomerative, unbiased clustering on these subtraction plots is performed using average linkage of samples to the known control, generating a dendogram that is used in the calling of genotypes (see [Vandersteen J G, Bayrak-Toydemir P, Palais R A, Wittwer C T, Identifying common genetic variants by high-resolution melting, Clin. Chem, 2007; 53(7): 1191-1198]; incorporated herein by specific reference). Although the method allows for automated genotyping of unknown samples, no real statistical information is provided to suggest the likely hood of an unknown sample being of a known genotype (discriminant analysis or “supervised learning”) nor does it allow for the determination of the number of alleles present, which could be useful for unknown mutation discovery (cluster analysis or “unsupervised learning”).
Thus, far researchers have been able to apply HRM to applications as diverse as human leukocyte antigen (HLA) typing (see [Zhou L, Vandersteen J, Wang L, Fuller T, Taylor M, Palais B, Wittwer C T, High-resolution DNA melting curve analysis to establish HLA genotypic identity, Tissue Antigens, 2004; 64(2): 156-164]; incorporated herein by specific reference), classification of organisms (see Jeffery N, Gasser R B, Steer P A, Noormohammadi A H, Classification of Mycoplasma synoviae strains using single-strand conformation polymorphism and high-resolution melting-curve analysis of the v1hA gene single-copy region, Microbiology, 2007; 153: 2679-2688]; incorporated herein by specific reference), detection and quantification of DNA methylation (see [Wojdacz T K, Dobrovic A. Melting curve assays for DNA methylation analysis. Methods Mol Bio. 2009; 507: 229-240]; incorporated herein by specific reference), identification of candidate predisposition genes (see [Saitsu H, Kato M, Mizuguchi T, Hamada K, Osaka H, Tohyama J, Uruno K, Kumada S, Nishiyama K, Nishimura A, Okada I, Yoshimura Y, Hirai S, Kumada T, Hayasaka K, Fukuda A, Ogata K, Matsumoto N, De novo mutations in the gene encoding STXBP1 (MUNC18-1) cause early infantile epileptic encephalopathy, Nat Genet., 2008; 40(6): 782-788]; incorporated herein by specific reference), somatic acquired mutation ratios (see [Krypuy M, Newnham G M, Thomas D M, Conron M, Dobrovic A, High resolution melting analysis for the rapid and sensitive detection of mutations in clinical samples: KRAS codon 12 and 13 mutations in non-small cell lung cancer, BMC Cancer, 2006; 6:295]; incorporated herein by specific reference), mutation discovery (see [Krypuy M, Ahmed A A, Etemadmoghadam D, Hyland S J; Australian Ovarian Cancer Study Group, DeFazio A, Fox S B, Brenton J D, Bowtell D D, Dobrovic A, High resolution melting for mutation scanning of TP53 exons 5-8, BMC Cancer, 2007; 7: 168-181]; incorporated herein by specific reference), allelic prevalence in a population (see [Polakova K M, Lopotova T, Klamova H, Moravcova J. High-resolution melt curve analysis: initial screening for mutations in BCR-ABL kinase domain. Leuk. Res. 2008; 32(8): 1236-1243]; incorporated herein by specific reference), and DNA fingerprinting (see [Gale N, French D J, Howard R L, McDowell D G, Debenham P G, Brown T, Rapid typing of STRs in the human genome by HyBeacon melting, Org. Biomol. Chem, 2008; 6(24): 4553-4559]; incorporated herein by specific reference). HRM allows for the discrimination of genotypes by comparing melt curve shapes and positions relative from one sample to another following amplification of a DNA fragment containing the alleles being investigated (see [Wittwer C T, Reed G H, Grundry C N, Vandersteen J G, Pryor R J, High-resolution genotyping by amplicon melting analysis using LCGreen, Clin. Chem, 2003; 49: 853-860]; incorporated herein by specific reference). The shape and positional variation of one melt curve to another is dependent on amplicon length, guanine-cytosine (GC) content, buffer conditions and the random generation of heteroduplexes, along with other reaction variables (see [Ririe K M, Rasmussen R P, Wittwer C T, Product differentiation by analysis of DNA melting curves during the polymerase chain reaction, Analytical Biochem., 1997; 245: 154-160] or [Wittwer C T, Reed G H, Grundry C N, Vandersteen J G, Pryor R J, High-resolution genotyping by amplicon melting analysis using LCGreen, Clin. Chem., 2003; 49: 853-860]; both incorporated herein by specific reference). Heteroduplex formation allows for the detection of heterozygotes and more complex variations within a sample. For the period of cooling during the polymerase chain reaction, the re-association of denatured strands can result in mismatches between alleles of minor nucleotide variations such as single nucleotide polymorphisms (SNPs) (see [Jensen M A, Straus N, Effect of PCR conditions on the formation of heteroduplex and single-stranded DNA products in the amplification of bacterial ribosomal DNA spacer regions, PCR Methods Appl., 1993; 3: 186-194]; incorporated herein by specific reference). These mismatches will denature at lower temperatures prior to the dissociation of the matched templates, and therefore, results in a melt curve with a double inflection (see [Reed G H, Wittwer C T, Sensitivity and specificity of single-nucleotide polymorphism scanning by high-resolution melting analysis, Clin. Chem., 2004; 50(10): 1748-1754]; incorporated herein by specific reference). Homozygous variations result in a curve positional change as the amplicon with the lowest energy between nucleotide pairing and neighbouring will denature earlier, however, this method is instrument dependent, which requires a temperature uniformity of less than 0.05° C. and a temperature resolution of less than 0.1° C. (see [Liew M, Pryor R, Palais R, Meadows C, Erali M, Lyon E, Wittwer C, Genotyping of single-nucleotide polymorphisms by high-resolution melting of small amplicons, Clin. Chem., 2004; 50(7): 1156-1164] or [Herrmann M G, Durtschi J D, Bromley L K, Wittwer C T, Voelkerding K V, Amplicon DNA melting analysis for mutation scanning and genotyping: cross-platform comparison of instruments and dyes, Clin. Chem., 2006; 52(3): 494-503]; both incorporated herein by specific reference). Current HRM software packages can plot these variations in curve shape and position; however, they do not offer the ability to statistically quantify these differences for both supervised and unsupervised data sets.
Therefore, it is an object of the present invention to overcome or ameliorate at least one of the disadvantages of the prior art, or to provide a useful alternative melt curve analysis.